Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/jianzhichun/emerge/monitorgit clone --depth 1 https://github.com/jianzhichun/emergeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/jianzhichun/emerge/monitor)<a href="https://agentmods.dev/commands/jianzhichun/emerge/monitor"><img src="https://agentmods.dev/badge/commands/jianzhichun/emerge/monitor.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.00889 |
| Opus 5 | $0.00000 | $0.00445 |
| Sonnet 5 | $0.00000 | $0.00178 |
| Haiku 4.5 | $0.00000 | $0.00089 |
Grade A, and why
monitor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/emerge:monitor — Agents-Team Monitor Mode
Spawn a per-runner watcher agent for every configured runner. Each watcher monitors its runner's EventBus for patterns and applies the stage→action popup protocol autonomously.
Always invoke the admin CLI via the Emerge plugin root.
Steps
-
Read configured runner profiles:
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/repl_admin.py" runner-status --prettyExtract the list of profile names (e.g.
mycader-1,mycader-2). If no runners are configured, inform the user and stop. -
Create the monitor team:
TeamCreate(team_name="emerge-monitors", description="Per-runner pattern watchers") -
Spawn one watcher per runner profile (replace
{profile}for each):Agent( subagent_type="general-purpose", team_name="emerge-monitors", name="{profile}-watcher", prompt=""" You are an emerge vertical monitor agent for runner: {profile}. Setup: 1. Start Monitor: command: "python3 ${CLAUDE_PLUGIN_ROOT}/scripts/watch_emerge.py --runner-profile {profile}" description: "pattern alert watcher — {profile}" persistent: true 2. You are now idle, waiting for pattern alerts. On [OperatorMonitor] alert notification: - Read `stage` and `intent_signature` from the alert. - Apply the stage→action protocol: - stage=explore → silent; record intent in notes if new - stage=canary → call runner_notify MCP tool: runner_notify( runner_profile="{profile}", ui_spec={"type": "choice", "title": "emerge — 可以接管了", "body": "[{intent_signature}] 已见 {occurrences} 次，接管？", "options": ["接管", "跳过", "停止学习"], "timeout_s": 15}) 接管 → icc_exec(intent_signature=<value from alert JSON field "intent_signature">) 停止学习 → intent freeze via repl_admin intent-set --set frozen=true - stage=stable → icc_exec(intent_signature=<value from alert JSON field "intent_signature">) silently AI uncertainty or knowledge distillation questions → call runner_notify with type=input: runner_notify( runner_profile="{profile}", ui_spec={"type": "input", "title": "emerge — 需要确认", "body": "<question>"}) Store answer in NOTES.md via notes.comment cockpit action. - SendMessage(team_lead, summary of action taken) - Return to idle. On shutdown_request from team lead: - Stop your Monitor. - Exit cleanly. """ )Repeat Agent spawn for each profile found in step 1.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 99 lines · 0 tokens per session scan A 15a243f2d947
monitor is a command published in the GitHub repository jianzhichun/emerge (107 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 889 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.